Continuous prediction of expected chip performance throuhout the production lifecycle

ABSTRACT

A system, method and computer program product for predicting at least one feature of at least one product being manufactured. The system receives, from at least one sensor installed in equipment performing one or more manufacturing process steps, at least one measurement of the feature of the product being manufactured. The system selects one or more of the received measurement of the feature of the product. The system estimates additional measurements of the feature of the product at a current manufacturing process step. The system creates a computational model for predicting future measurements of the feature of the product, based on the selected measurement and the estimated additional measurements. The system predicts the future measurements of the feature of the product based on the created computational model. The system outputs the predicted future measurements of the feature of the product.

BACKGROUND

The present application generally relates to manufacturing ofsemiconductor products. More particularly, the present applicationrelates to predicting measurement values of features of thesemiconductor products being manufactured.

A semiconductor chip or wafer (i.e. a collection of semiconductor chips)takes about three months to manufacture. While manufacturing thesemiconductor chip or wafer, thousands of measurements are obtained,from sensors attached to manufacturing equipment, to monitor quality ofthe semiconductor chip or wafer being manufactured. At eachmanufacturing step, 5% or 10% of total wafers being processed in themanufacturing step are sampled to obtain the measurements (e.g., area,yield rate, speed, etc.). A manufacturing step of a semiconductor chipor wafer includes, but is not limited to performing: deposition,etching, lithography, doping, etc.

Under current solutions, a user cannot determine whether thatsemiconductor chip or wafer being manufactured is going to result in asemiconductor chip or wafer that is in compliance with its product orperformance specification, or a semiconductor chip or wafer notcomplying with its product or performance specification.

As knowledge about potential issues relating to a product beingmanufactured, while it is being manufactured, is advantageous forquality control purposes, it would be highly desirable to be able togenerate or obtain predictions of future measurements of a productcurrently being manufactured, as it is being manufactured, so as tobetter assess that product's ultimate performance related metric andviability for commercial use.

SUMMARY

The present disclosure describes a system, method and computer programproduct for predicting at least one feature of at least one productbeing manufactured.

In one embodiment, there is provided a system for predicting at leastone feature of at least one product being manufactured. The systemreceives, from at least one sensor installed in at least one equipmentperforming one or more manufacturing process steps, at least onemeasurement of the at least one feature of the at least one productbeing manufactured. The system selects one or more of the at least onereceived measurement of the at least one feature of the at least oneproduct. The system estimates additional measurements of the at leastone feature of the at least one product at a current manufacturingprocess step. The system creates a computational model for predictingfuture measurements of the at least one feature of the at least oneproduct, based on the selected measurement and the estimated additionalmeasurements. The system predicts the future measurements of the atleast one feature of the product based on the created computationalmodel. The system outputs the predicted future measurements of the atleast one feature of the at least one product.

In a further embodiment, the at least one received measurement of the atleast one feature of the at least one product includes one or more of: aspeed measurement of the at least one product, a power consumptionmeasurement of the at least one product, a yield rate measurement of theat least one product, a leakage current measurement of the at least oneproduct, an area measurement of the at least one product, a capacitancemeasurement of the at least one product, and a resistance measurement ofthe at least one product.

In a further embodiment, the at least one product being manufacturedincludes one or more of: a semiconductor chip, a semiconductor wafer,and a semiconductor wafer lot.

In a further embodiment, the computing system obtains estimates of theadditional measurements of the at least one feature of the at least oneproduct by computing average values or mean values of the at least onereceived measurement of the at least one feature of other similar orsame products.

In a further embodiment, to create the computational model, the systemapplies a regression technique or machine learning technique to theselected measurement and the estimated additional measurements.

In a further embodiment, the system performs the step of receiving, thestep of selecting, the step of estimating, the step of creating, thestep of predicting and the step of outputting, in real-time at one ormore manufacturing process step.

In a further embodiment, the system sets thresholds for distinguishing aspecification compliant (i.e. normal) product, a slow product or a fastproduct. The system characterizes the product as the normal product, theslow product, or the fast product, according to the set thresholds. Thesystem optimizes the step of setting and the step of characterizing. Thesystem notifies a user of the predicted future measurements of theproduct, if the product is characterized as the slow product or the fastproduct.

In a further embodiment, the computing system updates the createdcomputational model whenever the computing system receives a newmeasurement of the at least one feature of the at least one product, andthe computing system updates the prediction of the future measurementsof the at least one feature of the product according to the updatedcomputational model.

In a further embodiment, the system updates the created computationalmodel every pre-determined time period, and system updates theprediction of the future measurements of the at least one feature of theproduct every pre-determined time period according to the updatedcomputational model.

In a further embodiment, the system determines a time interval in whichthe created computational model computes the future measurements of theat least one feature of the at least one product with accuracy.

In a further embodiment, to determine the time interval, the systemcompares two or more time intervals in which the created computationalmodel computes the future measurements. The system compares accuraciesof the computed future measurements of the two or more time intervals.The system selects an interval whose accuracy is greatest among the twoor more time intervals.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the present invention, and are incorporated in andconstitute a part of this specification.

FIG. 1 is a flow chart that describes method steps for predicting atleast one feature of at least one product being manufactured in oneembodiment.

FIG. 2 is a flow chart that describes method steps for multistepprediction method in one embodiment.

FIG. 3 is a flow chart that describes method steps for customizedboosted tree in one embodiment.

FIG. 4 a is a flow chart that describes method steps for determining athreshold(s) to evaluate whether a predicted speed of a product beingmanufactured is fast in one embodiment.

FIG. 4 b is a flow chart that describes method steps for determining athreshold(s) to evaluate whether a predicted speed of a product beingmanufactured is slow in one embodiment.

FIG. 5 is a flow chart that describes method steps for determining athreshold(s) to evaluate whether a predicted speed of a product beingmanufactured is normal in one embodiment.

FIG. 6 illustrates exemplary hardware configuration to run the methodsteps shown in FIGS. 1-5 in one embodiment.

FIG. 7 illustrates an exemplary manufacturing process of a product inone embodiment.

FIG. 8 illustrates exemplary measurements of products being manufacturedin one embodiment.

FIG. 9 illustrates a table that demonstrates an ability to predict, withaccuracy, a group of products whose speed is going to be faster than athreshold in one embodiment.

FIG. 10 illustrates a table that demonstrates an ability to determine abest time interval during which future measurements of a feature of aproduct being manufactured is going to be predicted.

DETAILED DESCRIPTION

FIG. 6 illustrates an exemplary hardware configuration of a computingsystem 600 that runs the method steps described in FIGS. 1-5. Thehardware configuration preferably has at least one processor or centralprocessing unit (CPU) 611. The CPUs 611 are interconnected via a systembus 612 to a random access memory (RAM) 614, read-only memory (ROM) 616,input/output (I/O) adapter 618 (for connecting peripheral devices suchas disk units 621 and tape drives 640 to the bus 612), user interfaceadapter 622 (for connecting a keyboard 624, mouse 626, speaker 628,microphone 632, and/or other user interface device to the bus 612), acommunication adapter 634 for connecting the system 600 to a dataprocessing network, the Internet, an Intranet, a local area network(LAN), etc., and a display adapter 636 for connecting the bus 612 to adisplay device 638 and/or printer 639 (e.g., a digital printer of thelike).

The computing system 600 accurately (e.g., more than 85% accuracy)predicts performance metrics (e.g., speed, power consumption, etc.) of aproduct being manufactured at every stage in its manufacturing process,before these metrics are known or can be measured, e.g., by a sensorassociated with a manufacturing machine or manufacturing process step.

Advantages of predicting at least one measurement of the feature of theproduct being manufactured, before the product is fully manufactured,include, but are not limited to:

-   -   (1) Fix products whose predicted performances deviate from their        product specification while manufacturing those products;    -   (2) Arrange products according to expected performance and        current demand; and    -   (3) Arrange products for each customer based on his/her required        product specification.

FIG. 1 is a flow chart that describes method steps for predicting atleast one feature (e.g., speed, area, etc.) of at least one productbeing manufactured in one embodiment. In one embodiment, the productbeing manufactured includes one or more of: a semiconductor chip, asemiconductor wafer, and a semiconductor wafer lot. At step 100, thecomputing system 600 receives, from at least one sensor installed in oroperating in conjunction with at least one equipment performing one ormore manufacturing process steps, at least one measurement of thefeature of the product being manufactured. The feature of the productincludes, but is not limited to: a speed of the product (e.g., a clockor oscillator frequency), a power consumption of the product, a yieldrate of the product, a leakage current of the product, a capacitance ofthe product, a resistance of the product, etc. The received real-timemeasurement of the feature of the product includes, but is not limitedto: a speed measurement (e.g., a clock frequency, an oscillator signaloutput, a logic delay time, etc.) of the product, a power consumptionmeasurement of the product, a yield rate measurement of the product, aleakage current measurement of the product, an area measurement of theproduct, a capacitance measurement of the product, and a resistancemeasurement of the product. The computing system 600 stores the receivedmeasurements, e.g., in a database (not shown). In one embodiment, thecomputing system 600 evaluates quality of the received measurement,e.g., by applying at least one known statistical test (e.g., T-test,etc.) on the received measurement.

Returning to FIG. 1, at step 105, the computing system 600 selects oneor more of the received measurement of the feature of the product beingmanufactured. In one embodiment, a different measurement of the featureof the product being manufactured is taken at each differentmanufacturing process step. While a product is being manufactured up toa certain manufacturing process step, there may be multiple (e.g., 100)different sensors activated that measure features of the product. Then,the product may have a sub-set of (e.g., at least 80) of differentmeasurements obtained from the sensors. Other products may have fewerdifferent measurements (e.g., less than 10) obtained from the sensors atthe same or different manufacturing process step.

FIG. 8 is a table 800 that specifies exemplary measurements of featuresof products being manufactured at a semi-conductor manufacturing plantfor a particular lot and wafer ID manufactured. For example, a“measurement 1” 810 (e.g., leakage current measurement) is obtained for“44” number of products being manufactured 815 but are not obtained for“6236” number of same or similar products being manufactured 820. Thus,94% of total products being manufactured 825 did not have the“measurement 1” data. A “measurement 3” 830 (e.g., power consumptionmeasurement) is obtained for “47” number of products being manufactured835 but are not obtained for “6267” number of same or similar productsbeing manufactured 840. Thus, 94% of total products being manufactured845 did not have the “measurement 3” data. Note that, in this exemplaryembodiment, as indicated in the “Fraction Missing” column 850, more than90% of total products being manufactured do not have each measurement.However, the computing system 600 is able to make predictions ofmeasurements of the feature of the product being manufactured, e.g., byrunning method steps in FIG. 1, even though more than 90% of totalproducts being manufactured do not have a corresponding measurement.

Returning to FIG. 1, at step 110, the computing system 600 estimatesadditional measurements of the feature of the product at a currentmanufacturing process step. For example, assume that a first productbeing manufactured does not have a measurement of a leakage current.However, if other similar or identical product(s) being manufactured oralready manufactured have available leakage current measurements at thecurrent manufacturing process step, then the computing systemadditionally estimates the leakage current measurement of the firstproduct. In one embodiment, the leakage current measurement estimate ofthe first product is obtained, e.g., by averaging the leakage currentmeasurements of the other similar or identical products, or bycalculating a mean value of the leakage current measurements of theother similar or same products.

At step 115, the computing system 600 creates a computational model forpredicting future measurements of the product being manufactured, basedon the selected measurement and the estimated additional measurements.In one embodiment, the computing system creates the computational model,e.g., by applying a regression technique (e.g., a linear regression,etc.) or a machine learning technique (e.g., an unsupervised learning,etc.) to the selected measurement and the estimated missingmeasurements. The system predicts the future measurements of the featureof the product using the created computational model. The system outputsthe predicted future measurements of the product being manufactured. Inone embodiment, the created computational model is used in performing amulti-step prediction or customized boosted tree prediction.

FIG. 2 is a flow chart that describes method steps of a multistepprediction method for predicting future measurements of the feature ofthe products being manufactured in one embodiment. At step 200, thecomputing system 600 collects a first set samples “S1” of products withknown and complete feature measurements. At step 210, the computingsystem 600 collects a second set of independent samples “S2” of thoseproducts for testing, in a manner that ensures no overlap between thecollected samples “S1” and “S2.” At step 220, the computing system 600randomly selects from the samples set “S1” and creates a further set ofsamples “S3.” At step 230, the computing system 600 creates acomputational model for predicting future measurements of those productsfrom the further set of samples “S3.” An example of a computationalmodel created by using a linear regression technique, includes, but isnot limited to: wt(l)×M(l)+ . . . +wt(j)M(j)=P(k), where j is the numberof measurements, wt( ) is a weight applied to each measurement, MO is ameasurement value evaluated at a measurement time, P(k) is a k-thproduct whose future measurement is predicted. Weights wt( ) are foundto minimize an error of the prediction. In one embodiment, theprediction error is estimated, e.g., by computing a mse (mean squareerror), or by computing a mad (mean absolute deviation) for thedifference between a actual measurement “T(j)” and predicted measurement“P(j).”

Returning to FIG. 2, at step 240, the computing system 600 repeats steps220-230 for k times such that there may be k number of computationalmodels created. At step 250, the computing system 600 averages resultsof the k number of computational models. In one embodiment, the averageof the k results is the predicted future measurement of the feature ofthe products being manufactured. Alternatively, the mean value of the kresults is the predicted future measurement of the feature of theproducts being manufactured.

FIG. 3 is a flow chart that describes method steps of a customizedboosted tree method for predicting future measurement of the feature ofthe products being manufactured in accordance with another embodiment.At step 300, the computing system 600 determines a sampling period forcreating the samples of a set “S1” and samples of a set “S2,” e.g., 90days of semiconductor wafer production. At step 310, the computingsystem determines a number of samples for a further set of samples “S3.”For example, the computing system 600 randomly selects the samples forset “S3,” all of which are from the samples of set “S1.” The number ofthe samples of set “S3” may be, for example, 10% of the number of thesamples in set “S1.” The samples of set “S3” includes samples ofmanufactured products whose predicted future measurements obtained, instep 250 in FIG. 2, were erroneous, i.e., the difference between theactual measurements and the predicted measurements is larger than apre-determined threshold.

Returning to FIG. 3, at step 320, the computing system 600 creates acomputation model from the samples set “S3,” by using a known regressiontechnique (e.g., linear regression, logistic regression, nonlinearregression, nonparametric regression, robust regression, stepwiseregression, etc.) or a known machine learning technique (e.g.,supervised learning, unsupervised learning, reinforcement learning,etc.). The created computational model includes, but is not limited to:a linear equation, a non-linear equation, a convex function, anon-convex function, etc. At step 330, the computing system 600 repeatssteps 310-320 k times. At step 340, the computing system 600 averagesresults of all computational models that tested well on the samples“S2,” i.e., the difference between those results and actual measurementsof the samples “S2” is less than a pre-determined threshold. In oneembodiment, the average of those results is the predicted futuremeasurement of the feature of the products being manufactured. Inanother embodiment, the mean value of the results is the predictedfuture measurement of the feature of the products. The computing system600 stores the predicted future measurement, e.g., in a database (notshown).

In one embodiment, the computing system 600 predicts the futuremeasurements of the feature of the products being manufactured, e.g.,based on the created computational model, before those products reach acertain manufacturing process step. In this embodiment, unless thecomputing system 600 performs predicting the future measurements ofthose products before that certain manufacturing process step, thecomputing system 600 may halt production of those products beyond thatcertain manufacturing process step. The computing system 600 may stop amanufacturing process step of those products until the computing system600 receives, from sensors associated with that manufacturing processstep, actual measurements of the feature of those products which arenecessary to predict the future measurements of the feature of thoseproducts.

Returning to FIG. 1, step 120, the computing system 600 performscategorizing of the wafers according to their predicted featuremeasurements. In one embodiment, computing system 600 performs settingthresholds to categorize the product being manufactured as one of: anormal product (i.e., a predicted speed of the product complies with theproduct specification), a slow product (i.e., a predicted speed of theproduct is slower than a threshold) or a fast product (i.e., a predictedspeed of the product is faster than a threshold). The systemcharacterizes the product as the normal product, the slow product, orthe fast product, according to the set thresholds. The system optimizesthe setting of thresholds, e.g., by running method steps in FIGS. 4-5which are described in detail below.

Further, continuing at step 125, before the product reaches the certainmanufacturing process step(s) (e.g., e.g., manufacturing step m (700)and/or manufacturing step n (710) shown in FIG. 7), or before the numberof products being manufactured reaches a certain operation number, forexample, 2280, 2070, etc., the computing system 600 notifies a user ofthe predicted future measurements of the product, e.g., by email, text,instant message, etc., if the product is characterized as the normal,slow product or the fast product.

FIG. 4 a is a flow chart that describes method steps for determining athreshold to evaluate and predict based on the model results whether aproduct being manufactured is a fast product in one embodiment. At step400, the computing system 600 builds a statistical model (e.g., thecomputational model created at step 230 in FIG. 2 or created at step 320in FIG. 3, etc.) based on first samples of complete products. At step405, the computing system 600 collects a separate test sample from thosecomplete products. In one embodiment, the separate test sample may becollected from either earlier or later completed semiconductor chips orwafers. The first samples and test samples do not overlap each other. Atstep 410, by using the created statistical model and the test samples,the computing system predicts target measurements of the feature of theproduct being manufactured, e.g., as described in FIGS. 2-3. At step415, for each product being manufactured, the computing system 600computes prediction errors, e.g., by comparing those target measurementsto actual measurements obtained from sensors in a manufacturing processstep of the product.

At step 420, for a subset of products being manufactured whoseprediction errors are computed larger than a pre-determined threshold,the computing system 600 computes a mse (mean square error) or mad (meanabsolute deviation) value for that subset. At step 425, the computingsystem 600 computes mse (or mad) for the subset of those products whosepredicted future measurements (e.g., predicted speeds, etc.) are abovethe mean value of the measurements of those first samples and/or testsamples. At step 430, the computing system 600 computes mse (or mad) forthe subset of those products whose predicted future measurements (e.g.,predicted speeds, etc.) are below the mean value of the measurements ofthose first samples and/or test samples.

At step 435, the computing system 600 computes an accuracy rateaccording to: the number of products, among the subset of the products,whose actual measurements are actually above the mean value, divided bythe number of products, among the subset of the products, whosepredicted future measurements were above the mean value. At step 440, byusing this computed accuracy rate, a user selects a threshold that canmost accurately determine the fast product. In other words, the userselects the threshold that maximizes the accuracy rate. The user mayalso consider expected costs and yield rates when selecting thethreshold to determine the fast product. The user can determine thethreshold based on domain information (e.g., product specification,etc.).

In one embodiment, the computing system 600 runs method steps in FIG. 4b to determine a threshold to evaluate whether a product beingmanufactured is a slow product. In this embodiment, steps 450-470 inFIG. 4 b are same as 400-420 in FIG. 4 a. Steps 485-490 in FIG. 4 b aresame as steps 435-440 in FIG. 4 a. However, at step 475, FIG. 4 b, thecomputing system 600 computes mse (or mad) for the subset of thoseproducts whose predicted future measurements (e.g., predicted speeds,etc.) are below the mean value of the measurements of those firstsamples and/or test samples. At step 480, the computing system 600 thencomputes mse (or mad) for the subset of those products whose predictedfuture measurements (e.g., predicted speeds, etc.) are above the meanvalue of the measurements of those first samples and/or test samples. Atstep 485, the computing system 600 computes an accuracy rate accordingto: the number of products, among the subset of the products, whoseactual measurements are actually below the mean value, divided by thenumber of products, among the subset of the products, whose predictedfuture measurements were below the mean value. At step 440, by usingthis computed accuracy rate, a user selects a threshold that can mostaccurately determine the slow product. In other words, the user selectsthe threshold that maximizes the accuracy rate.

FIG. 9 is an exemplary table 900 including data demonstrating an abilityof the computing system 600 to accurately (e.g., more than 85% accuracy)categorize example products being manufactured as fast or slow products,e.g., by running method steps in FIGS. 4 a-4 b. For example, as shown inthe table 900 in FIG. 9, regarding the test sample 1 (910), there was86% (i.e., (150/174)×100%) accuracy when the computing system 600categorizes products being manufactured as fast products. Regarding testsample 2 (920), there was 90% (i.e., 46/51×100%) accuracy when the whenthe computing system 600 categorizes products being manufactured as fastproducts. Regarding sample 3 (930), there was 91% (i.e., 71/77×100%)accuracy when the computing system 600 categorizes products beingmanufactured as fast products. An average measurement of a feature(e.g., speed, etc.) of test sample 1 was 13.02. An average measurementof a feature of test sample 2 was 13.17. An average measurement of afeature of high sample 3 wafers 940 (i.e., wafers, among sample 3, whichare categorized as fast products) was 13.57.

FIG. 5 is a flow chart that describes method steps for determining athreshold to evaluate whether a product being manufactured is a normalproduct in one embodiment. The computing system 600 runs method steps500-515 similarly as described herein with respect to the method steps400-415 in FIG. 4 a or steps 450-465 in FIG. 4 b, which are described indetail above. At step 520, a user specifies a normal range (includingupper and lower bounds) of a speed of the product being manufactured,e.g., based on a corresponding product specification. At step 525-530,the computing system 600 examines every possible range of the predictedfuture measurement of the product based on the test samples. Thecomputing system 600 computes an accuracy ratio, e.g., according to thenumber of products, among the subset of products, whose actualmeasurements are within the normal range, divided by the number ofproducts, among the subset of products, whose predicted futuremeasurements are within the normal range. At step 535, the computingsystem 600 selects a range, among all the possible ranges, whoseaccuracy ratio is the best to minimize the prediction error. Theselected range may cover a minimum number (e.g., 10 or 20, etc.) ofthose products being manufactured.

Upon characterizing the product being manufactured as a normal product,a fast product or the slow product, the computing system 600 notifies amanufacture of the product being manufactured, e.g., email, text,instant message, etc. Then, a user (e.g., a manufacturing/fabricationequipment operator, technician or engineer) may make an adjustment tothe manufacturing process of the product being manufactured or make anadjustment to the product being manufactured in order to adjust fast orslow product(s) as normal product(s).

Returning to FIG. 1, at step 130, the computing system 600 updates thecreated computational model whenever the computing system receives a newmeasurement of the feature of the product being manufactured. Then, thecomputing system 600 updates the prediction of the future measurementsof the at least one feature of the product, e.g., by running the updatedcomputational model. In one embodiment, the computing system 600 updatesthe created computational model every pre-determined time period. In oneembodiment, the computing system updates the prediction of the futuremeasurements of the at least one feature of the product everypre-determined time period, e.g., by running the updated computationalmodel.

FIG. 7 illustrates a diagram 750 depicting the updating of computationmodels in one embodiment. In FIG. 7, each manufacturing or test step maybe associated with particular number of manufacturing or test processesand particular measurements obtained during the particular manufacturingor test processes. For example, the manufacturing step m is associatedwith “25” manufacturing processes, and “29” measurements per a productare obtained during the “25” manufacturing processes. In FIG. 7, theremay a plurality of computational models to predict a future measurementof the feature of a product being manufactured. A manufacturing processstep may be associated with at least one computation model (e.g., Model1, or Model 2) that predicts a future measurement of a feature of aproduct being processed in that manufacturing process step. For example,according to FIG. 7, after a manufacturing process step n (700), thecomputing system 600 predicts the future measurement of the feature ofthe product being manufactured, e.g., based on a computational model 1.After a manufacturing process step m (710), the computing system 600predicts the future measurement of the feature of the product beingmanufactured, e.g., based on a computational model 2. These predictedmeasurements are compared to actual measurements obtained at a test stepo (715). Arrows from the manufacturing steps m and n to the test step orefer to manufacturing flows of products being manufactured. PSRO refersto a frequency measurement of Performance Sort Ring Oscillator in aproduct being manufactured. Based on these comparisons, thecomputational models (e.g., computational models 1-2) are updated 730 toreduce prediction errors. For example, if an original computation modelis a linear equation, the original computation model may be updated tohave a different slope and/or different constant term.

In one embodiment, the computing system determines a time interval inwhich the created computational model computes the future measurementsof the feature of the product being manufactured with accuracy.Specifically, the computing system 600 compares accuracies of thecomputed future measurements at the two or more time intervals. Thecomputing system 600 selects an interval whose accuracy is greatestamong the two or more time intervals. For example, FIG. 10 is anexemplary table 1000 that demonstrates that the computing system 600conducted future measurements predictions for a product feature at threedifferent time intervals. In this example, in FIG. 10, the interval 3(1005) may be the best time interval because by computing the futuremeasurements of products being manufactured according to the interval 3(1000), 45% of those future measurements were accurate.

In one embodiment, the computing system 600 performs the method steps inFIGS. 1-3 in real-time at one or more manufacturing process step. In oneembodiment, the computing system 600 performs the method steps in FIGS.4-5 in real-time. In one embodiment, the computing system 600continuously predicts future measurements of products being manufacturedthroughout their manufacturing lifecycle, e.g., by running method stepsin FIGS. 2-3.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with a system, apparatus, or device runningan instruction.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with asystem, apparatus, or device running an instruction.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may run entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which run via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerprogram instructions may also be stored in a computer readable mediumthat can direct a computer, other programmable data processingapparatus, or other devices to function in a particular manner, suchthat the instructions stored in the computer readable medium produce anarticle of manufacture including instructions which implement thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which run on the computeror other programmable apparatus provide processes for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more operable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be run substantiallyconcurrently, or the blocks may sometimes be run in the reverse order,depending upon the functionality involved. It will also be noted thateach block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for predicting at least one feature ofat least one product being manufactured, the method comprising:receiving, from at least one sensor installed in at least one equipmentperforming one or more manufacturing process steps, at least onemeasurement of the at least one feature of the at least one productbeing manufactured; selecting one or more of the at least one receivedmeasurement of the at least one feature of the at least one product;estimating additional measurements of the at least one feature of the atleast one product at a current manufacturing process step; creating acomputational model for predicting future measurements of the at leastone feature of the at least one product, based on the selectedmeasurement and the estimated additional measurements; predicting thefuture measurements of the at least one feature of the product based onthe created computational model; and outputting the predicted futuremeasurements of the at least one feature of the at least one product,wherein the step of receiving, the step of selecting, the step ofestimating, the step of creating, the step of predicting and the step ofoutputting are performed by a computing system that includes at leastone memory device and at least one processor device connected to thememory device.
 2. The method according to claim 1, wherein the at leastone received measurement of the at least one feature of the at least oneproduct includes one or more of: a speed measurement of the at least oneproduct, a power consumption measurement of the at least one product, ayield rate measurement of the at least one product, a leakage currentmeasurement of the at least one product, an area measurement of the atleast one product, a capacitance measurement of the at least oneproduct, and a resistance measurement of the at least one product. 3.The method according to claim 1, wherein the at least one product beingmanufactured includes one or more of: a semiconductor chip, asemiconductor wafer, and a semiconductor wafer lot.
 4. The methodaccording to claim 1, further comprising: obtaining estimates of theadditional measurements of the at least one feature of the at least oneproduct by computing average values or mean values of the at least onereceived measurement of the at least one feature of other similar orsame products.
 5. The method according to claim 1, wherein the step ofcreating the computational model includes: applying a regressiontechnique or machine learning technique to the selected measurement andthe estimated additional measurements.
 6. The method according to claim1, wherein the computing system performs the step of receiving, the stepof selecting, the step of estimating, the step of creating, the step ofpredicting and the step of outputting, in real-time at one or moremanufacturing process step.
 7. The method according to claim 2, furthercomprising: setting thresholds for distinguishing a normal product, aslow product or a fast product; characterizing the product as the normalproduct, the slow product, or the fast product, according to the setthresholds; optimizing the step of setting and the step ofcharacterizing; and notifying a user of the predicted futuremeasurements of the product, if the product is characterized as the slowproduct or the fast product.
 8. The method according to claim 1, furthercomprising: updating the created computational model whenever thecomputing system receives a new measurement of the at least one featureof the at least one product; and updating the prediction of the futuremeasurements of the at least one feature of the product according to theupdated computational model.
 9. The method according to claim 1, furthercomprising: updating the created computational model everypre-determined time period; and updating the prediction of the futuremeasurements of the at least one feature of the product every thepre-determined time period according to the updated computational model.10. The method according to claim 1, further comprising: storing thepredicted future measurements of the at least one feature of the atleast one product and the at least one received measurement of the atleast one feature of the at least one product in a database.
 11. Themethod according to claim 1, further comprising: evaluating quality ofthe at least one received measurement by applying at least onestatistical test on the at least one received measurement.
 12. Themethod according to claim 1, further comprising: determining a timeinterval in which the created computational model computes the futuremeasurements of the at least one feature of the at least one productwith accuracy.
 13. The method according to claim 12, wherein the step ofdetermining further comprises: comparing two or more time intervals inwhich the created computational model computes the future measurements;comparing accuracies of the computed future measurements of the two ormore time intervals; and selecting an interval whose accuracy isgreatest among the two or more time intervals.
 14. A system forpredicting at least one feature of at least one product beingmanufactured, the system comprising: at least one memory device; and atleast one processor, wherein the processor is configured to receive,from at least one sensor installed in at least one equipment performingone or more manufacturing process steps, at least one measurement of theat least one feature of the at least one product being manufactured;select one or more of the at least one received measurement of the atleast one feature of the at least one product; estimate additionalmeasurements of the at least one feature of the at least one product ata current manufacturing process step; create a computational model forpredicting future measurements of the at least one feature of the atleast one product, based on the selected measurement and the estimatedadditional measurements; predict the future measurements of the at leastone feature of the product based on the created computational model; andoutput the predicted future measurements of the at least one feature ofthe at least one product.
 15. The system according to claim 14, whereinthe at least one received measurement of the at least one feature of theat least one product includes one or more of: a speed measurement of theat least one product, a power consumption measurement of the at leastone product, a yield rate measurement of the at least one product, aleakage current measurement of the at least one product, an areameasurement of the at least one product, a capacitance measurement ofthe at least one product, and a resistance measurement of the at leastone product.
 16. The system according to claim 14, wherein the at leastone product being manufactured includes one or more of: a semiconductorchip, a semiconductor wafer, and a semiconductor wafer lot.
 17. Thesystem according to claim 14, wherein the processor is furtherconfigured to: obtain estimates of the additional measurements of the atleast one feature of the at least one product by computing averagevalues or mean values of the at least one received measurement of the atleast one feature of other similar or same products.
 18. The systemaccording to claim 14, wherein to create the computational model, theprocessor is configured to: apply a regression technique or machinelearning technique to the selected measurement and the estimatedadditional measurements.
 19. The system according to claim 15, whereinthe processor is further configured to: set thresholds fordistinguishing a normal product, a slow product or a fast product;characterize the product as the normal product, the slow product, or thefast product, according to the set thresholds; optimize the step ofsetting and the step of characterizing; and notify a user of thepredicted future measurements of the product, if the product ischaracterized as the slow product or the fast product.
 20. The systemaccording to claim 14, wherein the processor is further configured to:update the created computational model whenever the computing systemreceives a new measurement of the product; and updating the predictionof the future measurements of the at least one feature of the productaccording to the updated computational model.
 21. The system accordingto claim 14, wherein the processor is further configured to: update thecreated computational model every pre-determined time period; andupdating the prediction of the future measurements of the at least onefeature of the product every the pre-determined time period according tothe updated computational model.
 22. The system according to claim 14,wherein the processor is further configured to: store the predictedfuture measurements of the at least one feature of the at least oneproduct and the at least one received measurement of the at least onefeature of the at least one product in a database.
 23. The systemaccording to claim 14, wherein the processor is further configured to:evaluate quality of the at least one received measurement by applying atleast one statistical test on the at least one received measurement. 24.A computer program product for predicting at least one feature of atleast one product being manufactured, the computer program productcomprising a storage medium readable by a processing circuit and storinginstructions run by the processing circuit for performing a method, themethod comprising: receiving, from at least one sensor installed in atleast one equipment performing one or more manufacturing process steps,at least one measurement of the at least one feature of the at least oneproduct being manufactured; selecting one or more of the at least onereceived measurement of the at least one feature of the at least oneproduct; estimating additional measurements of the at least one productat a current manufacturing process step; creating a computational modelfor predicting future measurements of the at least one feature of the atleast one product, based on the selected measurement and the estimatedadditional measurements; predicting the future measurements of the atleast one feature of the at least one product based on the createdcomputational model; and outputting the predicted future measurements ofthe at least one feature of the at least one product.
 25. The computerprogram product according to claim 24, wherein the at least one receivedmeasurement of the at least one feature of the at least one productincludes one or more of: a speed measurement of the at least oneproduct, a power consumption measurement of the at least one product, ayield rate measurement of the at least one product, a leakage currentmeasurement of the at least one product, an area measurement of the atleast one product, a capacitance measurement of the at least oneproduct, and a resistance measurement of the at least one product.